Performance of the SEAPROG 

Prognosis Variant of the Forest 

Vegetation Simulator

Michael H. McClellan and Frances E. Biles

United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

Research Paper

PNW-RP-555

October 2003
Authors Michael H. McClellan is a research forest ecologist and Frances E. Biles is 

a geographer, Forestry Sciences Laboratory, 2770 Sherwood Lane, Suite 2A, 

Juneau, AK 99801-8545. 
Abstract McClellan, Michael H.; Biles, Frances E. 2003. Performance of the SEAPROG 

prognosis variant of the forest vegetation simulator. Res. Pap. PNW-RP-555. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 15 p.

This paper reports the first phase of a recent effort to evaluate the performance and 

use of the FVS-SEAPROG vegetation growth model. In this paper, we present our 

evaluation of SEAPROG’s performance in modeling the growth of even-aged stands 

regenerated by clearcutting, windthrow, or fire. We evaluated the model by comparing 

model predictions to observed values from two sets of long-term permanent plots. We 

examined six variables: trees per acre, quadratic mean diameter, basal area per acre, 

height of the largest 40 trees per acre, cubic-foot volume per acre, and board-foot 

volume per acre. The differences between observed and predicted values were large 

enough to have important implications for the interpretation and use of the model’s 

predictions. Of even greater importance was the evidence for considerable bias in 

quadratic mean diameter, basal area, height, and volume, all of which were systematically underestimated. Our results appear to validate the concerns expressed by users.

Keywords: Growth and yield, forest management, growth projection, modeling, southeast Alaska.
Summary SEAPROG is a Prognosis variant of the Forest Vegetation Simulator, and it is the 

primary vegetation growth model used for forest management in southeast Alaska. 

SEAPROG is widely available and there exists a large corps of trained users, but the 

model is used much less than expected. We identified two reasons limiting the use of 

SEAPROG: user perceptions that the model predictions frequently are unrealistic and 

that the model has not been calibrated to predict the outcome of the more complex 

silvicultural systems being prescribed today, such as two-age and uneven-age management. We concluded that SEAPROG use would continue to decline if these issues 

were not resolved. This paper reports the first phase of our recent effort to evaluate 

the performance and use of SEAPROG. We present our evaluation of SEAPROG’s 

performance in predicting the growth of even-aged stands regenerated after clearcutting, windthrow, or fire. We evaluated the model by comparing model predictions 

to observed values from two sets of long-term permanent plots. We examined six 

variables: trees per acre, quadratic mean diameter, basal area per acre, height of the 

largest 40 trees per acre, cubic-foot volume per acre, and board-foot volume per acre. 

The differences we found between observed and predicted values were large enough 

to have important implications for the interpretation and use of the model’s predictions. 

Of even greater importance was the evidence for considerable bias in quadratic mean 

diameter, basal area, height, and volume, all of which were systematically underestimated. Our results appear to validate the concerns expressed by users.
1

Introduction This paper reports on a recent effort to evaluate the performance and use of FVSSEAPROG, a vegetation growth model used in southeast Alaska (Dixon and others 

1992). We prepared it in response to concerns voiced by many SEAPROG users in 

southeast Alaska—silviculturists who had tried to use SEAPROG, but found its predictions unrealistic, its user interface difficult to master, and its ability to model complex 

stand structures limited. We were asked to evaluate the performance of SEAPROG 

for even-age and uneven-age management and, where possible, compare its predictions to observed growth data. In this paper, we present our evaluation of SEAPROG’s 

performance in modeling the growth of even-aged stands regenerated after clearcutting, windthrow, or fire. We hope that our findings will lead to further improvements to 

SEAPROG and wider use of growth models as management tools.

Forest managers are being challenged to produce an increasing number of values 

from a decreasing area of forested lands available for active management. Most forest 

values derive from the combination of physical setting and vegetation composition and 

structure, which fundamentally affect the function of the system and the production of 

goods and services we desire. The manipulation of forest vegetation is often the common denominator reaching across discussions of wildlife and fish habitat, visual quality, wood production, recreational opportunities, biological diversity, and a host of other 

forest management issues. 

Because most values of concern are at least partly vegetation dependent, it follows 

that any analysis and evaluation of management options must proceed from reliable predictions of vegetation composition and structure. The range of management 

options considered and the complexity of silvicultural prescriptions have increased 

dramatically over the past decade, and this has created a demand for more capable 

vegetation dynamics models. For example, concerns over deer habitat in southeast 

Alaska have led to increased emphasis on maintaining understory plant communities 

(Zaborske and others 2002). Controversies over clearcutting have led to wider use 

of partial cutting and the need to predict the growth of stands with varying levels and 

patterns of green-tree retention. Managers are being asked to integrate and evaluate 

tradeoffs among multiple values such as wood production, wood quality, and understory plant diversity and abundance. In this context, there is a clear need for flexible, 

reliable, easy-to-use models of vegetation development that predict the dynamics of 

trees and understory plants, as well as key interactions with disturbance agents. 

The primary vegetation model for southeast Alaska is FVS-SEAPROG, a geographic 

variant of the Forest Vegetation Simulator (Dixon and others 1992). The SEAPROG 

variant was developed in 1984 by staff at the Forest Management Service Center in 

Fort Collins, Colorado, in cooperation with Wilbur (Bill) Farr and others at the Forestry 

Sciences Laboratory in Juneau. SEAPROG is an adaptation of the Prognosis stand 

development model (Stage 1973), which was originally developed for the Inland 

Empire area of Idaho and Montana. Data used to develop the SEAPROG model 

came from forest inventories on the Tongass National Forest (Juneau, Stikine, Sitka, 

and Prince of Wales areas), the Makah Indian Reservation, and the Queen Charlotte 

Islands. Other data sources included young-growth stand exams, young-growth surveys, and Farr’s young-growth stand-density study (DeMars 2000). The geographic 

area covered by SEAPROG includes the Tongass National Forest, other forested 

lands in southeastern Alaska, coastal British Columbia, the Queen Charlotte Islands, 

and the northwestern tip of the Olympic Peninsula (Dixon and others 1992).
2 

With suitable calibration, an individual-tree growth model such as SEAPROG should 

be able to predict growth with some degree of confidence for many forest types and 

stand structures. SEAPROG can predict the growth of common conifers in southeast 

Alaska (Sitka spruce (Picea sitchensis (Bong.) Carr.), western hemlock (Tsuga heterophylla (Raf.) Sarg.), western redcedar (Thuja plicata Donn ex D. Don), yellow-cedar 

(Chamaecyparis nootkatensis (D. Don) Spach), mountain hemlock (Tsuga mertensiana (Bong.) Carr.) as well as less common conifers such as Pacific silver fir (Abies 

amabilis (Dougl. ex Loud.) Dougl. ex Forbes), subalpine fir (Abies lasiocarpa (Hook.) 

Nutt.), shore (lodgepole) pine (Pinus contorta Dougl. ex Loud. var. contorta), and 

white spruce (Picea glauca (Moench) Voss). The hardwoods red alder (Alnus rubra

Bong.) and black cottonwood (Populus balsamifera L. ssp. trichocarpa (Torr. & Gray 

ex Hook.) Brayshaw) are also included (Dixon and others 1992, Van Dyck 1999). The 

flexibility of this model is intended to permit the simulation of alternative management 

practices in a wide array of vegetation types.

During the Tongass Land Management Plan (TLMP) revision, the accuracy of 

SEAPROG growth and yield projections was a concern. Some planners suspected 

that the model overestimated growth because much of the data used to calibrate the 

model came from low-elevation, moderately to highly productive sites. On the other 

hand, users from the Alaska Native corporations, whose timberlands tend to be highly 

productive, reported that the model underestimated growth on their lands, when compared with observed growth.

We know of at least three prior efforts to evaluate or validate SEAPROG performance 

and one study that provides an indication of its ability to predict growth on wet, lowproductivity sites. First, between 1985 and 1987, model predictions were compared 

with observed growth on six of the “Taylor plots” (a set of nine permanent plots established in older even-aged stands in the 1920s by R.F. Taylor) (Dixon and others 

1992). In that test, the first 10 years of growth data were used to calibrate the model 

for each plot. Predicted trees per acre varied from 3 percent less to 11 percent more 

than observed values, dominant tree heights varied from 8 percent less to 10 percent 

more, and quadratic mean diameters varied from 6 percent less to 2 percent more 

(Dixon and others 1992). Golnick and others (1995) reviewed the results from this test 

and detected no apparent bias in board-foot volume predictions. Second, from the late 

1980s through the early 1990s, Wilbur Farr worked with the Washington office timber 

management group to evaluate and improve the performance of the mortality model. 

Unfortunately, at the time of Farr’s death this work was unfinished. Third, in 1995, as 

part of the TLMP revision, the model was reviewed with respect to the reliability of 

growth-and-yield predictions in young stands, the effect of projections on the allowable sale quantity (ASQ), and the suitability of the model for predicting the results from 

partial cutting of old-growth forests (Golnick and others 1995). Although an extensive 

statistical analysis was not carried out, the reviewers concluded that the growth-andyield projections for young-growth stands were reliable and that the ASQ was not 

highly sensitive to changes in those predictions. However, they also concluded that 

SEAPROG was not suited to projections of the results of partial cutting because of the 

absence of data from partially cut stands to validate the model results (Golnick and 

others 1995).
3

A recent study of the productivity of forested wetlands (Julin and D’Amore 2003) 

showed that SEAPROG growth predictions agreed closely with observed growth in 

18 stands 15 to 46 years after clearcutting. The authors concluded that on these 

poorly productive sites, representing the least productive of sites actively managed 

for wood production, the model shows no tendency to overestimate or underestimate 

growth.

Based on these prior evaluations of SEAPROG, our first objective was an evaluation 

of SEAPROG’s strengths and weaknesses in modeling relatively simple even-age 

management options. The second objective was to evaluate model performance in 

more complex management scenarios involving uneven-age management. We expected to complete the first step and to move on quickly to the second. As it turned 

out, we found significant problems with the model performance in even-age management scenarios, and we chose to focus on those issues. In this paper, we report our 

findings from:

1. A survey of SEAPROG users in order to compile a list of perceived strengths 

and weaknesses of the simulator and to determine how resource managers are 

using it.

2. A comparison of SEAPROG growth predictions with actual stand-growth data 

from two long-term young-growth studies, including trees per acre, quadratic 

mean diameter, basal area, height, cubic-foot volume, and board-foot volume.

During 1998 we canvassed SEAPROG users in the USDA Forest Service (USFS) 

Alaska Region, Alaska Native corporations, and the private sector to determine the 

frequency of model use, how the model was being used, and what shortcomings users 

perceived. This user group was identified by using attendee lists from local USFSsponsored SEAPROG training sessions and from a directory of USFS silviculturists 

in the Alaska Region. We made no attempt to quantify responses by issue or type of 

problem noted—the responses were simply compiled and summarized in a narrative 

(see below).

Simulations—We used growth data from Farr’s stand-density study and the Taylor 

plots to evaluate SEAPROG predictions for moderately to highly productive sites. 

Stands in Farr’s study are currently 25 to 115 years old, and the Taylor plots range 

from 120 to 175 years old. We used data from 259 of 271 plots located at 67 installations in southeast Alaska. Of the 259 plots, 208 had been thinned. Twelve plots were 

grossly overstocked (2,000 to 6,667 trees per acre, quadratic mean diameter 1.9 to 

4.5 in), and we excluded them from the analysis (fig. 1). Detailed information on both 

sets of plots and their treatment histories may be found in DeMars (2000). We produced two tree tables for each plot: the first containing observed tree data collected 

immediately following plot establishment and treatment (if any), the second containing 

observed tree data from the latest plot remeasurement. All calculations of stand-level 

variables (e.g., trees per acre, basal area, volume) were done within SEAPROG. The 

first set of observations (at plot establishment) was used to provide the starting conditions for the SEAPROG growth simulations. The second set of observations (latest 

remeasurement) was compared with the SEAPROG predictions. 

Methods

User Perceptions

Comparisons of 

Observed and 

Predicted Growth
4 

A projection cycle is a period for which SEAPROG predicts changes in tree characteristics, and a complete SEAPROG projection may include one or more cycles (Wykoff 

and others 1982). Predicted values may be biased when projection-cycle lengths other 

than 10 years are used (Wykoff and others 1982). To determine the appropriate 

number of years to run the model for each plot, we calculated for each plot the number 

of growing seasons from the initial to the final measurement date. Partial growing 

years sometimes occurred in our data because it is not always possible to remeasure 

plots outside the growing season (roughly April through September). Because the 

model accepts only whole numbers for cycle lengths, the number of growing seasons 

was rounded to the nearest whole growing season. This resulted in some discrepancies between the final simulation year and the final remeasurement year, but the final 

output better reflected the actual growth period. When the partial growing year 

equaled 0.5, the run duration was simply assigned to the difference between initial 

year and final year (table 1). Once we determined the growth period 

in whole years, the number of cycles was determined by using cycle lengths as close 

to 10 years as possible. Preference was given to longer and fewer growth periods. 

For example, five cycles of 11 years and one of 10 years would be used for a growth 

period of 65 years, rather than five cycles of 9 years and two of 10 years.

SEAPROG allows users to adjust the growth-model coefficients by calibrating the 

model with prior-growth information. Calibration requires height-growth data for 

roughly 5 years and diameter-growth data for roughly 10 years prior to the start of 

the simulation. Data are required from five trees for each species to be calibrated. 

Although we had the data available, we chose not to use the calibration option because users indicated they did not typically have the past growth data and, hence, 

did not use that feature. Our intent was to evaluate SEAPROG performance as 

practitioners typically used it.

Figure 1—Predicted trees per acre versus observed values, 

with the overstocked plots included. The line represents the 

equation y = x, the ideal case where the predicted values 

equal the observed values.
5

Table 1—Examples of cycle-length assignments used in the simulations

Installation Plot First measure Last measure Growth Cycle

number number date date period lengths

- - - - - - Years - - - - - -

49 2680 7/27/84 10/8/98 14.3 14 = 7, 7

46 3180 8/9/76 5/19/98 21.4 21 = 11, 10

50 2750 6/14/84 10/10/98 14.6 15 = 8, 7

20 1740 7/2/76 9/27/98 22.5 22 = 11, 11

36 2340 8/12/75 6/12/99 23.5 24 = 12, 12

48 2040 9/20/79 9/17/85 6.0 6 = 6

Analysis of results—We compared observed and predicted values for trees per 

acre (TPA), quadratic mean diameter (QMD), basal area per acre (BA), height of the 

largest 40 trees per acre (HT40), total-stem cubic-foot volume per acre (CFV), and 

board-foot volume per acre (based on 32-foot logs, BFV). For each pair of values we 

calculated a residual, r

i

 as follows:

r

i

 = Yi – Yi ,

where Yi is the predicted value and Yi is the observed value. This differs from the typical residuals (Yi

 – Yi) computed in the course of fitting linear models with least-squares 

methods where, by definition, the mean of the residuals (r ) is zero:

r = = 0 .

Our use of residuals in model validation can yield a nonzero residual mean, and we 

used the sign and magnitude of this mean as an estimate of model bias, B:

B = r = .

This method of computing residuals yields an intuitively satisfying estimate of bias, 

where a negative bias is associated with model underestimates and a positive bias is 

associated with model overestimates. We computed two estimates of the total average 

error of the model predictions, the mean absolute residual (MAR) and the root mean 

squared residual (RMSR):

MAR = , 

RMSR = . 

The MAR weights each residual equally, ignoring sign, and the RMSR gives relatively 

more weight to large residuals that result from gross model misbehavior.

ˆ

ˆ

ˆ
6 

Results

User Perceptions

We produced scatter diagrams of observed versus predicted values, residuals versus 

observed values, and residuals versus selected site and stand attributes (stand age, 

site index, elevation, and slope). We examined the graphs for the patterns and magnitudes of departures from the ideal case where the SEAPROG predictions would equal 

the observed values (that is, predicted = observed, residual = 0).

In general, SEAPROG has not been used much in southeast Alaska except for some 

limited modeling of young-growth thinning. Many of the silviculturists reported using SEAPROG only for the preparation of their certification prescriptions. The most 

common reasons given for the limited use were difficulty of use, problems with the 

program used to summarize stand examination data for input to SEAPROG, lack of 

user confidence in model predictions, and the perceived inability to reliably predict the 

results of using silvicultural systems other than even-age management with clearcutting. The users identified slope and elevation as the independent variables most often 

associated with unrealistic model output. These variables should be the first priority for 

a sensitivity analysis. Users also questioned the reliability of the ingrowth and mortality functions, and stated that too many trees survive in moderately dense modeled 

stands.

Users also noted that SEAPROG mainly supported the analysis of conifer response, 

and did not adequately model the response of many hardwood species or the response of shrubs and ground-layer vegetation. Most of the issues users were dealing 

with were related to silvicultural treatment effects on wildlife and visual quality. Users 

seeking to maintain deer habitat wanted the ability to model understory shrub and herbaceous plant response to canopy openings and to schedule precommercial thinning 

to maximize release of understory plants and minimize hemlock regeneration.

Many users wanted SEAPROG to predict the effects of various levels of green-tree 

retention on residual tree growth, growth of the new cohort, and species composition. 

Furthermore, they wanted SEAPROG to link more easily with the Stand Visualization 

System (SVS) (McGaughey, n.d.), the Landscape Management System (LMS) 

(McCarter and others 1998), and geographic information systems (GIS) in order to visualize how individual stands and landscapes will appear immediately after treatment 

and as the stands develop.

When modeling the growth of mixed western hemlock-Sitka spruce stands from 15 

to 20 years old to harvest age, users reported that, contrary to field observations, 

SEAPROG gradually eliminates hemlock from the stand. The effect becomes noticeable by 40 to 50 years and becomes more pronounced over time. The same users 

reported that, in their experience, about one-third of the area within naturally regenerated stands will be covered with codominant western hemlock at 60 years. They 

suspected that the model assumes a uniform mixture of hemlock and spruce within 

the stand and that the spruce is allowed to out-compete the hemlock because of the 

superior growth rate of spruce. In real stands, the distribution of trees by species may 

be more clumped and variable, which allows hemlock to persist.

Trees per acre—The residuals ranged from -287 to 659 TPA and their mean (bias) 

was 30 TPA, indicating that SEAPROG predictions were slightly higher on average 

than the observed trees per acre. Estimation of total error yielded a mean absolute 

residual of 45 TPA and a root mean squared residual of 103 TPA. Examination of 

the scatter diagrams (figs. 2a and 2b) revealed a change in the model behavior at 

a threshold of roughly 1,250 TPA. Below that density, SEAPROG generally overestimated TPA, in many cases by several hundred trees per acre. At higher densities 

Comparisons of 

Observed and 

Predicted Growth
7

SEAPROG began to underestimate TPA, and the divergence between observed and 

predicted values increased greatly with increasing observed TPA. From our initial 

results that included the 12 overstocked plots, it appeared that the model consistently 

reduced density to below 3,000 TPA (fig. 1).

Figure 2a—Predicted trees per acre versus observed values (overstocked plots excluded). The line represents the equation y = x, the 

ideal case where the predicted values equal the observed values.

Figure 2b—Trees-per-acre residuals plotted over observed values.
8 

Quadratic mean diameter—The residuals ranged from -4.8 to 1.8 in and their mean 

(bias) was -1.1 in, indicating that SEAPROG predictions were lower on average than 

the observed diameters. Estimation of total error yielded a mean absolute residual of 

1.3 in and a root mean squared residual of 1.6 in. Examination of the scatter diagrams 

(figs. 3a and 3b) revealed that SEAPROG consistently overestimated QMD when the 

observed QMD was less than 5 in, usually underestimated QMD when the observed 

QMD was between 5 and 16 in, and consistently underestimated QMD when the observed QMD was greater than 16 in. 

Figure 3a—Predicted quadratic mean diameter versus observed values. The 

line represents the equation y = x, the ideal case where the predicted values 

equal the observed values.

Figure 3b—Quadratic mean diameter residuals plotted over observed values.
9

Basal area—The residuals ranged from -118 to 109 ft2/ac and their mean (bias) was 

-20.4 ft2/ac, indicating that SEAPROG predictions were generally lower than the 

observed basal areas. Estimation of total error yielded a mean absolute residual of 

32.7 ft2/ac and a root mean squared residual of 40.2 ft2/ac. Examination of the scatter 

diagrams (figs. 4a and 4b) did not disclose any marked change in the model’s behavior over the range of observed basal area. SEAPROG tended to underestimate basal 

area across the entire range, with perhaps a slightly greater tendency to underestimate at basal areas greater than 300 ft2/ac.

Figure 4a—Predicted basal area per acre versus observed values. The 

line represents the equation y = x, the ideal case where the predicted values equal the observed values.

Figure 4b—Basal area residuals plotted over observed values.
10 

Figure 5a—Predicted height of the largest 40 trees per acre versus 

observed values. The line represents the equation y = x, the ideal case 

where the predicted values equal the observed values.

Figure 5b—Height residuals plotted over observed values.

Top height—The residuals ranged from -25 to 26 ft and their mean (bias) was -1.8 ft, 

indicating that SEAPROG predictions were lower on average than the observed tree 

heights. Estimation of total error yielded a mean absolute residual of 4.7 ft and a root 

mean squared residual of 6.3 ft. Examination of the scatter diagrams (figs. 5a and 5b) 

showed that SEAPROG tended to underestimate tree heights across the entire range 

of observed tree heights, but it was interesting to note that Taylor (older) plots accounted for the five most extreme height overestimates.
11

Cubic-foot volume—The residuals ranged from -3,826 to 3,989 ft3/ac and their 

mean (bias) was -407 ft3/ac, indicating that SEAPROG predictions were generally 

lower than the observed cubic-foot volume per acre. Estimation of total error yielded 

a mean absolute residual of 982 ft3/ac and a root mean squared residual of 1,259 

ft3/ac. The scatter diagrams (figs. 6a and 6b) showed that as the observed volume 

increased, the model’s underestimation of volume became more pronounced.

Figure 6a—Predicted cubic-foot volume per acre versus observed values. 

The line represents the equation y = x, the ideal case where the predicted 

values equal the observed values.

Figure 6b—Cubic-foot volume residuals plotted over observed values.
12 

Board-foot volume—The residuals ranged from -21,834 to 18,414 board feet per acre 

(bf/ac) and their mean (bias) was -3,622 bf/ac, indicating that SEAPROG predictions 

were lower on average than the observed board-foot volume per acre. Estimation of 

total error yielded a mean absolute residual of 5,057 bf/ac and a root mean squared 

residual of 7,060 bf/ac. As with cubic-foot volume, examination of the scatter diagrams 

(figs. 7a and 7b) revealed an increasing tendency to underestimate board-foot volume 

as the observed volume increased.

Figure 7b—Board-foot volume residuals plotted over observed values. 

(Note: mbf/ac = thousand board feet per acre.)

Figure 7a—Predicted board-foot volume per acre versus observed 

values. The line represents the equation y = x, the ideal case where the 

predicted values equal the observed values. (Note: mbf/ac = thousand 

board feet per acre.)
13

Effects of site and stand attributes—We examined the distribution of residuals over 

total stand age, site index, elevation, and slope, but did not find the effects of slope 

and elevation reported by users. Most of the plots were younger than 85 years, and 

we observed no strong patterns in residuals below that age. The small number of plots 

older than 85 years made it difficult to determine whether the height overestimation for 

the Taylor plots was due to their age or some feature of those plots, but we noted 11 

plots from 100 to 120 years old where height was underestimated, so it does not seem 

likely that this is purely an age effect. When the plot site index (50-year basis) was 105 

ft or greater, we observed a much greater tendency to underestimate QMD, BA, and 

both volume measures. The distributions of TPA and height residuals were not related 

to site index. Elevation had little effect on any of the residual distributions, which is not 

surprising given the limited range of elevation represented by the sample (all but four 

plots were below 500 feet above sea level). Slope varied more widely (0 to 50 percent), but it had no discernible effect on residuals.

The low level of use and the lack of user confidence in SEAPROG predictions are 

troubling. Annual training sessions hosted by the Alaska Region and the continuing 

improvements to the model’s graphical user interface should make potential users 

more comfortable with using the model. Recently improved linkages to GIS and visualization systems should address user concerns with the difficulty of displaying model 

predictions with those systems. Attempts to create a broader group of users—including wildlife biologists, fish biologists, and others concerned with the effects of vegetation management on forest resources—could also lead to wider use and application. 

These efforts will likely be in vain, however, until users view SEAPROG as reliable and 

able to model a wider range of silvicultural options and the response of all layers of forest vegetation.

No model is perfect, and users must expect and tolerate some deviation from a perfect 

relationship between observed and predicted values. Hopefully the deviation will be 

of a reasonable magnitude for the purpose at hand and will be free of systematic bias, 

that is, overall the deviations cancel out each other and the mean residual is close to 

zero. The magnitudes of the residuals we observed in our analysis—for all variables—

were large enough to have important implications for the interpretation and use of the 

model’s predictions. Of even greater importance was the evidence for substantial bias 

in quadratic mean diameter, basal area, height, and volume, all of which were systematically underestimated. Our results appear to validate the concerns expressed by users and suggest that the first task at hand is a search for the sources of the observed 

errors and a revision or recalibration of SEAPROG.

We recommend three additional tasks that could lead to greater reliability and use of 

SEAPROG. The first would be to conduct a sensitivity analysis of the independent 

site and stand variables used by the model to determine where unrealistic results occur. With the empirical data available to us, we were unable to duplicate the behavior 

reported by users. This was due to the limited range of elevation, slope, and stand 

age represented in the Farr and Taylor plots. To support future model development 

and validation efforts, additional permanent plots could be added to the Farr study to 

expand the range of site conditions represented. The second task is to evaluate existing information on tree growth and yield following partial cutting of old-growth stands 

and, where feasible, use it to calibrate SEAPROG. Examples include results from the 

retrospective Alternatives to Clearcutting (ATC) study (Deal and Tappeiner 2002), the 

experimental portion of the ATC study (McClellan and others 2000), and operational 

Discussion
14 

partial retention harvests. Finally, there is a need to incorporate into the model information on the response of understory plants to silvicultural treatments. This includes 

both intermediate treatments in young-growth stands and regeneration harvests in 

old-growth stands.

Another important effort is underway to incorporate the dynamics of damage agents 

into SEAPROG. Paul Hennon is working with the Forest Health Technology Enterprise 

Team (a detached unit of State and Private Forestry, Washington office) to incorporate 

a recently developed model of mistletoe spread (Trummer 1996, Trummer and others 

1998) and a model of decay development following bole wounding of Sitka spruce and 

western hemlock (Hennon and DeMars 1997). Mistletoe and decay fungi are major 

damage agents in southeast Alaska, and this effort should improve the utility of the 

model.

The authors wish to recognize the contributions of Don DeMars and Larry Bednar to 

the early stages of this project. Tim Max provided some excellent advice that helped 

us clear out the statistical thickets and focus on a few simple, clear measures of model 

performance. Gary Dixon, David Marshall, Nathan Poage, and Jim Russell provided 

technical reviews of this paper.

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters

Feet (ft) .3048 Meters

Acres (ac) .405 Hectares

Square inches (in2) 645 Square millimeters

Square feet (ft2) .0929 Square meters

Square miles (mi2) 2.59 Square kilometers

Cubic feet (ft3) .0283 Cubic meters

Square feet per acre (ft2/ac) .229 Square meters per hectare

Cubic feet per acre (ft3/ac) .06997 Cubic meters per hectare

Trees per acre 2.471 Trees per hectare

Deal, R.L.; Tappeiner, J.C. 2002. The effects of partial cutting on stand structure 

and growth of western hemlock–Sitka spruce stands in southeast Alaska. Forest 

Ecology and Management. 159: 173–186.

DeMars, D.J. 2000. Stand-density study of spruce-hemlock stands in southeast 

Alaska. Gen. Tech. Rep. PNW-GTR-496. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 60 p.

Dixon, G.; Johnson, R.R.; Schoeder, D. 1992. Southeast Alaska/coastal British 

Columbia (SEAPROG) Prognosis variant of the Forest Vegetation Simulator. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, WO-TM Service 

Center. 75 p.

Golnick, D.; Zaborske, R.; Julin, K. 1995. Tongass plan revision growth and yield 

estimates (timber assessment task 3) (Draft). Juneau, AK: U.S. Department of 

Agriculture, Forest Service, Tongass National Forest. 11 p.

Hennon, P.E.; DeMars, D.J. 1997. Development of wood decay in wounded western 

hemlock and Sitka spruce in southeast Alaska. Canadian Journal of Forest 

Research. 27: 1971–1978.

Acknowledgments

Metric Equivalents

Literature Cited
15

Julin, K.R.; D’Amore, D.V. 2003. Tree growth on forested wetlands following 

clearcutting in southeastern Alaska. Western Journal of Applied Forestry. 

18: 30–34.

McCarter, J.B.; Wilson, J.S.; Baker, P.J.; Nelson, C.E. 1998. Landscape 

management system user’s manual. Seattle, WA: University of Washington, 

College of Forest Resources, Landscape Management Project. 97 p.

McClellan, M.H.; Swanston, D.N.; Hennon, P.E. [and others]. 2000. Alternatives 

to clearcutting in the old-growth forests of southeast Alaska: study plan and 

establishment report. Gen. Tech. Rep. PNW-GTR-494. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

40 p.

McGaughey, R.J. [N.d.]. Stand visualization system. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. http://forsys.cfr.

washington.edu/svs.html. (27 June 2003).

Stage, A.R. 1973. Prognosis model for stand development. Gen. Tech. Rep. INT-137. 

Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest 

and Range Experiment Station. 32 p.

Trummer, L.M. 1996. Modeling hemlock dwarf mistletoe (Arceuthobium tsugense

subsp. tsugense) spread and intensification in mature uneven aged forests in 

southeast Alaska. Corvallis, OR: Oregon State University. 77 p. M.S. thesis.

Trummer, L.M.; Hennon, P.E.; Hansen, E.M.; Muir, P.M. 1998. Modeling the 

incidence and severity of hemlock dwarf mistletoe in 110-year-old wind-disturbed 

forests in southeast Alaska. Canadian Journal of Forest Research. 28: 1501–1508.

Van Dyck, M.G., comp. 1999. Keyword reference guide for the Forest Vegetation 

Simulator. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Forest 

Management Service Center. 93 p.

Wykoff, W.R.; Crookston, N.L.; Stage, A.R. 1982. User’s guide to the Stand 

Prognosis Model. Gen. Tech. Rep. INT-133. Logan, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 

112 p.

Zaborske, R.R.; Hauver, R.N.; McClellan, M.H.; Hanley, T.A. 2002. Understory 

vegetation development following commercial thinning in southeast Alaska: 

preliminary results from the Second-Growth Management Area Demonstration 

Project. In: Parker, S.; Hummel, S., comps. Beyond 2001: a silvicultural odyssey 

to sustaining terrestrial and aquatic ecosystems—Proceedings of the national 

silviculture workshop. Gen. Tech. Rep. PNW-GTR-546. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest Research Station: 

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